GMO North America | Europe | Asia-Pacific Ten Lessons (Not?) Learnt James Montier Asset Allocation
Lesson I: Markets aren’t efficient A long litany of bad ideas: CAPM Alpha and Beta Black and Scholes Risk management Mark-to market M&M dividend and capital structure irrelevance Shareholder Value Regulatory regime Most insidious aspects of EMH are the advice on sources of outperformance Inside information (illegal in most places) If you can see the future better than everyone else Also tells us that opportunities will be fleeting GMO 1
GMO -3 -2 -1 Not a drop of evidence that we can forecast anything at all 0 1 2 3 4 5 6 1982 1983 1984 1985 1986 1987 1988 Economist forecast 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Actual result 2006 2007 2008 2009
Forecast error over time: US and European markets 2001-2006, % GMO
Analyst expected returns (via target prices) and actual returns (US, %) 50 50 Analysts’ forecast Analysts’ forecast 40 40 Actual outturn Actual outturn 30 30 20 20 10 10 0 0 -10 -10 -20 -20 -30 -30 -40 -40 -50 -50 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 GMO
Lesson II: Relative Performance is a dangerous game Homo Ovinus GMO 5
-3 -2 -1 GMO 0 1 2 3 4 5 Institutional investors vs. US market (weight difference) Small Large Value Growth High Momo Low Momo High Accruals Low Accruals Source: Lewellen (2009) High issue Low issue
Many active managers aren’t even trying! Lewellen concludes: “Quite simply, institutions overall seem to do little more than hold the market portfolio. Their aggregate portfolio almost perfectly mimics the value-weighted index... Institutions overall take essentially no bet on any of the most important stock characteristics known to predict returns, like B/M, momentum, or accruals. The implication is that, to the extent that institutions’ holdings deviate from the market portfolio, they seem to bet primarily on idiosyncratic returns – bets that aren’t particularly successful. Another implication is that institutions, in aggregate, don’t exploit anomalies in the way they should.” GMO
Why not? Career and Business risk (Homo Ovinus rides again) Cohen et al conclude Cohen et al ‘Best Ideas’ “The poor overall performance of 2 0 mutual fund managers… is not due to a lack 1 8 of stock-picking ability, but rather to institutional factors that encourage them to 1 6 overdiversify.” 1 4 1 2 1 0 8 6 4 2 0 B e s t Id e a s M a r k e t GMO
Performance around hiring and firing decisions (%) GMO
Lesson III: This time is never different 3.5 3 2.5 2 1.5 1 0.5 1 12 23 34 45 56 67 78 89 100 111 122 M onths GMO
Mill/Minsky/Kindleberger framework for bubbles Critical Stage/Financial Distress Credit Creation Displacement Revulsion Euphoria GMO
Why don’t we see bubbles? Black Swans Predictable Surprise – Unpredictable – At least some where aware of the – Massive Impact problem – Ex-post rationalization – The problem gets worse over time – Eventually explodes into crisis Five impediments to recognising predictable surprises: (i) Over-optimism (ii) The illusion of control (iii) Self-serving bias (iv) Myopia (v) Inattentional blindness GMO
Lesson IV: Valuation Matters (in the long run) Source: GMO GMO
Buy when it’s cheap: if not then, when? Buying at 10x G&D PE Date of Purchase % decline from purchase to market trough Months to market trough Months before mkt return to purchase level 1917.09 -16 3 18 1931.10 -53 8 21 1942.03 -4 1 3 1982.03 -1 4 6 Average -19 4 12 US market 1881-2008 Subsequent Average Return Worst Subsequent Return % of times subsequent return positive 1 Yr 2 Yr 3 Yr 5 Yr 10 Yr 1 Yr 2 Yr 3 Yr 5 Yr 10 Yr 1 Yr 2 Yr 3 Yr 5 Yr 10 Yr 13 21 34 41 67 101 -24 -26 -18 -11 -38 80 92 91 94 92 10 26 37 43 80 93 -24 -11 -72 3 -38 82 92 90 100 95 9 34 41 52 93 82 -16 15 5 18 8 83 100 100 100 100 5 134 112 126 243 87 131 102 125 242 85 100 100 100 100 100 Source: GMO GMO
GMO 10 20 30 40 50 60 Graham and Dodd PE (S&P500) 0 1881 1885 1890 1894 1899 1903 1908 1913 1917 1922 1926 1931 1936 1940 1945 1949 1954 1958 1963 1968 1972 1977 1981 1986 1991 1995 Source: GMO 2000 2004 2009
Bottom up valuation: What would Ben think? % of stocks passing BG conditions 25 Now Mar-09 Nov-08 20 15 10 5 0 UK Europe US Japan Asia GMO
Lesson V: Wait for the “fat pitch” 600 500 400 300 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 Source: Plottt et al GMO
2005 Source: NYSE 2000 1995 1990 1985 1980 1975 1970 1965 ADHD and stock holding periods 1960 1955 1950 1945 1940 1935 1930 1925 GMO 1920 9 8 7 6 5 4 3 2 1 0 10
Lesson VI: Sentiment matters 12 10 Low Sentiment High Sentiment 8 6 4 2 0 Low PE High PE Source: GMO GMO
Exploiting sentiment (US 1962-2000) 2 When sentiment is low: Buy young, volatile, unprofitable firms When sentiment is high: Buy old, stable, profitable firms Sentiment Low 1.5 Sentiment High 1 0.5 0 -0.5 -1 -1.5 Age (old vs young) Volatility (high vs low) Profitability (high vs low) Source: Baker and Wurgler GMO
Sentiment suggests need for caution 55 50 45 40 35 30 25 20 15 96 97 98 99 00 01 02 03 04 05 06 07 08 09 ADVISORS SENTIMENT BEARISH Source: DATASTREAM GMO
Lesson VII: Leverage can’t turn a bad investment good • Leverage can’t turn a bad investment good, but it can turn a good one bad • Limits staying power • Can turn a temporary impairment (i.e. price volatility) into a permanent impairment of capital • Beware of financial innovation…often just leverage in a thinly veiled disguise GMO
Lesson VIII: Beware of over-quantification Ben Graham: “Mathematics is ordinarily considered as producing precise and dependable results; but in the stock market the more elaborate and abstruse the mathematics the more uncertain and speculative are the conclusions we draw there from…Whenever calculus is brought in, or higher algebra, you could take it as a warning that the operator was trying to substitute theory for experience, and usually also to give to speculation the deceptive guise of investment.” Keynes: “With a free hand to choose coefficients and time lag, one can, with enough industry, always cook a formula to fit moderately well a limited range of past facts. I think it all hocus - but everyone else is greatly impressed, it seems, by such a mess of unintelligible figures.” Munger: “It seems like higher mathematics with false precision should help you, but it doesn’t. They teach that in business schools because, well, they’ve got to do something!” There are no points for elegance in the real world. GMO
Risk isn’t a number Risk isn’t volatility, it is the permanent impairment of capital Volatility creates the opportunity. Keynes again: “It is largely fluctuations which throw up the bargains and the uncertainty due to fluctuations which prevents other people from taking advantage of them.” Think about risk as a trinity: Valuation risk Business risk Financing risk GMO
Lesson IX: There is no substitute for skepticism Santayana: “Skepticism is the chastity of the intellect, and it is shameful to surrender it too soon or to the first comer: there is nobility in preserving it coolly and proudly.” We are in the rejection game. GMO
Lesson X: Benefits of cheap insurance Insurance is often disliked in investing, because of the nature of the cash flows, but that often makes it cheap! Examples: Inflation insurance Moral hazard/market melt up insurance GMO
Long-term volatility 75 65 55 45 35 25 15 5 91 94 97 00 03 06 09 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 Source: SG, Bloomberg GMO
Lessons (not) learnt I. Markets aren’t efficient II. Relative performance is a fool’s game III. This time is never different IV. Valuation matters V. Wait for the fat pitch VI. Sentiment matters VII. Leverage can’t make a bad investment good VIII. Beware of over-quantification IX. There is no substitute for skepticism X. Don’t underestimate the use of cheap insurance GMO
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